1. Field of the Invention
The present invention relates in general to a radial basis function neural network and in particular to such a neural network incorporating filtering to assure that successfully mapped neighborhoods are excluded from later node influence.
2. Description of the Prior Art
Referring to the drawings, there is shown in FIG. 1, a typical prior art Radial Basis Function (RBF) neural network 1. The RBF neural network 1 is a combination of an input layer consisting of the nodes labeled 10 a single hidden layer 2 with the radial basis functions and a linear output layer 3. The linear output layer 3 consist of a plurality of weighting functions that are labeled 6a, 6b, 6c . . . 6n while the hidden layer 2 consists of a number of non-linear activation nodes 10. The non-linear activation functions of the hidden layer nodes 10 are based on the Euclidean distance between an input vector and a weight vector. The responses of the linear output layer 4 are added at a summing point 8 to form an output signal O.
A variety of patents disclose improvements to neural networks. For example, U.S. Pat. No. 5,717,832 to Steimle and U.S. Pat. No. 5,740,326 to Boulet et al. are related and disclose a form of blocking to simplify neural networks. U.S. Pat. Nos. 6,216,119 and 6,647,377 to Jannarone, based on the same disclosure, disclose a neural network having the capability to learn and predict in real time. U.S. Pat. Nos. 6,351,711 and 6,539,304 to Chansarkar, also based on the same disclosure, disclose a GPS receiver incorporating a neural network. U.S. Pat. No. 6,453,206 to Soraghan, et al. discloses a radial basis function network having a function generator incorporating trigonometric terms. U.S. Pat. No. 6,516,309 to Eberhart, et al. discloses a neural network which removes processing elements from the definition of the neural network in response to corresponding activation parameters satisfying certain criteria. U.S. Pat. No. 6,725,208 to Hartman, et al. discloses a Bayesian neural network incorporating a form of blocking.
As shown by the above examples, the prior art relating to neural networks is highly developed. However, a need remains for further improvement in order to prevent neighborhoods successfully mapped with a neural network from later node influence.